Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states.
Identifieur interne : 000149 ( Main/Exploration ); précédent : 000148; suivant : 000150Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states.
Auteurs : Itaf Ben Slimen [Tunisie] ; Larbi Boubchir [France] ; Hassene Seddik [Tunisie]Source :
- Journal of biomedical research [ 1674-8301 ] ; 2020.
Abstract
Epileptic seizures are known for their unpredictable nature. However, recent research provides that the transition to seizure event is not random but the result of evidence accumulations. Therefore, a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic patients. Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes, spikes, and the amplitude. In this study, spike rate is used as the indicator to anticipate seizures in electroencephalogram (EEG) signal. Spikes detection step is used in EEG signal during interictal, preictal, and ictal periods followed by a mean filter to smooth the spike number. The maximum spike rate in interictal periods is used as an indicator to predict seizures. When the spike number in the preictal period exceeds the threshold, an alarm is triggered. Using the CHB-MIT database, the proposed approach has ensured 92% accuracy in seizure prediction for all patients.
DOI: 10.7555/JBR.34.20190097
PubMed: 32561696
PubMed Central: PMC7324272
Affiliations:
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first="Itaf Ben" last="Slimen">Itaf Ben Slimen</name><affiliation wicri:level="1"><nlm:affiliation>Centre de Recherche et de Production Research Lab., Ecole Nationale Supérieure des Ingénieurs de Tunis, University of Tunis, Tunis 1008, Tunisia.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>Centre de Recherche et de Production Research Lab., Ecole Nationale Supérieure des Ingénieurs de Tunis, University of Tunis, Tunis 1008</wicri:regionArea><placeName><settlement type="city">Tunis</settlement><region nuts="2">Gouvernorat de Tunis</region></placeName></affiliation></author><author><name sortKey="Boubchir, Larbi" sort="Boubchir, Larbi" uniqKey="Boubchir L" first="Larbi" last="Boubchir">Larbi Boubchir</name><affiliation wicri:level="3"><nlm:affiliation>Laboratoire d'Informatique Avancée de Saint-Denis Research Lab., University of Paris 8, Saint-Denis, Cedex 93526, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Laboratoire d'Informatique Avancée de Saint-Denis Research Lab., University of Paris 8, Saint-Denis, Cedex 93526</wicri:regionArea><placeName><region type="region" nuts="2">Île-de-France</region></placeName></affiliation></author><author><name sortKey="Seddik, Hassene" sort="Seddik, Hassene" uniqKey="Seddik H" first="Hassene" last="Seddik">Hassene Seddik</name><affiliation wicri:level="1"><nlm:affiliation>Centre de Recherche et de Production Research Lab., Ecole Nationale Supérieure des Ingénieurs de Tunis, University of Tunis, Tunis 1008, Tunisia.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>Centre de Recherche et de Production Research Lab., Ecole Nationale Supérieure des Ingénieurs de Tunis, University of Tunis, Tunis 1008</wicri:regionArea><placeName><settlement type="city">Tunis</settlement><region nuts="2">Gouvernorat de Tunis</region></placeName></affiliation></author></analytic><series><title level="j">Journal of biomedical research</title><idno type="ISSN">1674-8301</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Epileptic seizures are known for their unpredictable nature. However, recent research provides that the transition to seizure event is not random but the result of evidence accumulations. Therefore, a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic patients. Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes, spikes, and the amplitude. In this study, spike rate is used as the indicator to anticipate seizures in electroencephalogram (EEG) signal. Spikes detection step is used in EEG signal during interictal, preictal, and ictal periods followed by a mean filter to smooth the spike number. The maximum spike rate in interictal periods is used as an indicator to predict seizures. When the spike number in the preictal period exceeds the threshold, an alarm is triggered. Using the CHB-MIT database, the proposed approach has ensured 92% accuracy in seizure prediction for all patients.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32561696</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>28</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">1674-8301</ISSN><JournalIssue CitedMedium="Print"><Volume>34</Volume><Issue>3</Issue><PubDate><Year>2020</Year><Month>Feb</Month><Day>17</Day></PubDate></JournalIssue><Title>Journal of biomedical research</Title><ISOAbbreviation>J Biomed Res</ISOAbbreviation></Journal><ArticleTitle>Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states.</ArticleTitle><Pagination><MedlinePgn>162-169</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.7555/JBR.34.20190097</ELocationID><Abstract><AbstractText>Epileptic seizures are known for their unpredictable nature. However, recent research provides that the transition to seizure event is not random but the result of evidence accumulations. Therefore, a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic patients. Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes, spikes, and the amplitude. In this study, spike rate is used as the indicator to anticipate seizures in electroencephalogram (EEG) signal. Spikes detection step is used in EEG signal during interictal, preictal, and ictal periods followed by a mean filter to smooth the spike number. The maximum spike rate in interictal periods is used as an indicator to predict seizures. When the spike number in the preictal period exceeds the threshold, an alarm is triggered. Using the CHB-MIT database, the proposed approach has ensured 92% accuracy in seizure prediction for all patients.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Slimen</LastName><ForeName>Itaf Ben</ForeName><Initials>IB</Initials><AffiliationInfo><Affiliation>Centre de Recherche et de Production Research Lab., Ecole Nationale Supérieure des Ingénieurs de Tunis, University of Tunis, Tunis 1008, Tunisia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Boubchir</LastName><ForeName>Larbi</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Laboratoire d'Informatique Avancée de Saint-Denis Research Lab., University of Paris 8, Saint-Denis, Cedex 93526, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Seddik</LastName><ForeName>Hassene</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Centre de Recherche et de Production Research Lab., Ecole Nationale Supérieure des Ingénieurs de 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Seddik</name></country><country name="France"><region name="Île-de-France"><name sortKey="Boubchir, Larbi" sort="Boubchir, Larbi" uniqKey="Boubchir L" first="Larbi" last="Boubchir">Larbi Boubchir</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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